Artificial intelligence has entered the workplace promising something highly desirable: saving time and increasing productivity.

And, in many cases, it really can help.

A report, presentation, email, or summary that used to take hours can now be produced in just a few minutes.

But a new problem is emerging in this scenario:

What happens when content is produced quickly, but isn't actually ready?

This is where the term Workslop comes in.

The concept describes AI-generated content that appears professional and complete at first glance, but is superficial, incomplete, not very useful, or requires significant review.

The result is a paradox:

The person who produced the content saves time, but someone else may end up spending even more time correcting the work.

And this raises an important question for any company:

Does saving time individually necessarily mean increasing team productivity?

 

What Is Workslop?

Workslop is a term used to describe content produced with artificial intelligence that appears to be ready, but does not provide enough quality or context to fulfill its purpose.

It can take many forms:

• visually appealing presentations with little relevant information;
• long, unfocused reports;
• summaries that leave out important information;
• generic texts that need to be rewritten;
• code produced without the necessary context;
• documents that appear complete but require extensive review.

The problem isn't simply using AI.

The problem occurs when a tool is used to produce something quickly without ensuring that the result actually solves the problem.

Research conducted by BetterUp Labs in partnership with the Stanford Social Media Lab specifically investigated this phenomenon.

 

Time Doesn't Disappear. It Changes Hands.

Imagine a simple situation.

An employee receives a task that would normally take one hour.

They use an AI tool and deliver the work in 10 minutes.

It looks like a productivity gain.

But the content reaches someone else.

That person needs to:

read → understand → identify problems → correct → add information → rework.

In the end, work that should have taken one hour may consume even more time.

That's why Workslop is an important topic for managers.

Time saved by one person can turn into additional work for someone else.

BetterUp Labs points out that Workslop can create additional work cycles, duplicated efforts, and loss of trust within teams.

 

How Much Can Workslop Cost a Company?

The numbers presented by BetterUp Labs help illustrate the scale of the problem.

In a survey of 1,150 full-time office workers in the United States, conducted in September 2025 in partnership with the Stanford Social Media Lab:

• 40% said they had received Workslop in the previous month;
• each occurrence took, on average, around 2 hours to resolve;
• the estimated impact reached US$186 per employee per month;
• in an organization with 10,000 employees, the estimated annual cost would reach US$9 million.

These numbers do not mean that every use of AI creates waste.

Quite the opposite.

The point is to understand that production speed is not the same as productivity.

 

Producing Faster Doesn't Necessarily Mean Producing Better

This is perhaps the most important point in this discussion.

Imagine two situations.

Situation 1

An employee takes:

2 hours → correct delivery → work completed.

Situation 2

An employee takes:

20 minutes → incomplete delivery → another person reviews it for 1.5 hours → the work is corrected.

Which situation was more productive?

The second one appears faster.

But when considering the work performed by the entire team, perhaps it wasn't.

This shows why productivity shouldn't be measured solely by how quickly a task is completed.

Other factors also need to be considered:

• quality;
• rework;
• total time invested;
• number of people involved;
• final outcome.

 

Workslop Can Create a False Sense of Productivity

This is another important risk.

A company might observe:

“We're producing much more content.”

But producing more documents, presentations, reports, or messages doesn't necessarily mean generating more value.

It's possible to increase the volume of work while actual productivity remains the same or even decreases.

BetterUp describes this phenomenon as a kind of illusion of progress: the material looks professional, but someone still needs to perform the analysis, add context, and make corrections.

This creates an important distinction between:

Activity

and

Results.

A team can be extremely busy and still produce very little meaningful output.

 

Who Pays the Cost of Rework?

The Workslop problem becomes even more interesting when we look at the workflow.

Imagine:

Person A

Produces quickly.

↓

Person B

Reviews.

↓

Person C

Corrects.

↓

Person A

Needs to explain it again.

↓

Team

Loses time.

This process creates a cost that often doesn't appear directly in any report.

It's not an obvious financial expense.

It's work time consumed by rework.

And that's precisely why this type of waste can be difficult to identify.

 

Workslop Isn't Exclusively an AI Problem

It's important to make this distinction.

Artificial intelligence didn't create rework.

Companies have been dealing with:

• inefficient processes;
• unnecessary meetings;
• unclear communication;
• duplicated tasks;
• lack of context;
• errors;
• manual activities;
• scattered information.

What changes with AI is the speed at which certain types of content can be produced.

This means that a poorly structured task can be completed more quickly — but the original problem still exists.

In other words:

Automating an activity doesn't necessarily eliminate the waste associated with it.

 

How Can Companies Prevent AI From Creating More Rework?

The solution isn't simply to ban the use of artificial intelligence.

BetterUp Labs highlights the importance of establishing clear guidelines, accountability for outcomes, and using technology to support collaboration, rather than simply using it to avoid doing the work.

In practice, a few questions can help:

1. Is the content actually ready?

Before sending it, it's important to check whether the material meets the original objective.

2. Is there enough context?

Content that appears complete can still be useless if it doesn't take into account the context of the company, client, or project.

3. Will the person receiving the material have additional work?

It's a simple but powerful question.

4. Was the time saved actually a real gain?

It's not enough to ask:

“How long did it take me to produce this?”

It's also necessary to consider:

“How much time did the team have to spend before the work was actually completed?”

 

Productivity Isn't Just About Doing Things Faster

The Workslop phenomenon raises a broader question about how companies measure productivity.

For a long time, productivity was associated with:

more tasks → more speed → more work completed.

But today's work environment requires a broader perspective.

A productive team isn't necessarily the one that:

• sends more messages;
• produces more documents;
• attends more meetings;
• completes more tasks.

It's the one that can turn time, knowledge, and resources into meaningful results.

That's why, when we talk about productivity, we need to look beyond the number of activities completed.

We need to understand how work actually happens.

 

What Does Workslop Teach Us About Management?

The main lesson may not actually be about artificial intelligence.

It's about productivity management.

If a company wants to understand whether it is becoming more efficient, it needs to look at the entire process.

Where is time being spent?

Which activities consume the most time?

Where does rework occur?

Which processes create bottlenecks?

Which activities could be better organized?

And, most importantly:

Does the time saved at one stage actually represent a gain for the entire team?

That's the difference between optimizing a task and improving organizational productivity.

 

Workslop and the Future of Productivity

Artificial intelligence will likely continue transforming the way we work.

But the next stage of this transformation shouldn't simply be:

“How can we do everything faster?”

Perhaps the more important question is:

“How can we ensure that the time saved actually creates value?”

Because a company can produce faster and still waste time.

It can automate tasks and still generate rework.

It can increase the volume of deliverables without increasing results.

And it can appear more productive without actually being more productive.

Workslop is a reminder that productivity isn't just about speed.

It's about results.

 

How Can Monitoo Help With This?

For managers, tracking productivity means looking beyond the perception that a team is simply “busy.”

Monitoo provides data related to computer usage, activities, applications, websites, idle periods, and productivity, helping managers identify patterns and potential bottlenecks.

The goal isn't simply to discover who is working and who isn't.

It's to understand how time is being used and turn that information into a better foundation for management decisions.

And this same logic applies to Workslop:

It's not enough to know that a task was completed. It's important to understand how much work was actually required for it to be completed.

 

Conclusion

Workslop represents a new type of waste that can emerge when artificial intelligence tools are used without sufficient context, review, or accountability for the outcome.

Technology can save minutes when producing content.

But if someone else needs to spend hours correcting that material, the individual gain can become a loss for the team.

That's why the question shouldn't simply be:

“How much time did AI save?”

But:

“How much work was actually completed?”

This change in perspective can be essential for companies that want to use technology without turning apparent productivity into real waste.

 

Frequently Asked Questions About Workslop

What does Workslop mean?

Workslop is a term used to describe AI-generated content that appears finished or professional but lacks sufficient usefulness, context, or quality and ends up creating additional work for the people who receive it.

Is Workslop any content produced by AI?

No. The term specifically refers to content that appears good but fails to deliver the necessary value and creates rework. Using AI to produce useful, reviewed, and contextualized content does not necessarily mean producing Workslop.

Can Workslop affect productivity?

Yes. According to BetterUp Labs' research, Workslop can generate rework, duplicated efforts, wasted time, and impacts on trust between colleagues.

How can companies reduce Workslop?

Measures highlighted by BetterUp include establishing clear guidelines for AI use, maintaining human accountability for outcomes, and using technology to enhance collaboration rather than simply avoid work.

 

Source

BetterUp Labs — Workslop: The Hidden Cost of AI-Generated Busywork